Differential Network Analysis: A Statistical Perspective
This is an incremental review article summarizing existing methods for differential network analysis, aimed at researchers in statistics and biology.
The paper reviews recent statistical machine learning methods for inferring networks and identifying structural changes, primarily motivated by biological applications where such changes predict diseases and provide insights into disease mechanisms.
Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures.